Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	128,145
2	South Dakota	122,363
3	Utah	108,118
4	Rhode Island	108,025
5	Tennessee	104,447
6	Arizona	104,218
7	Wisconsin	101,699
8	Iowa	101,236
9	Nebraska	98,681
10	Oklahoma	98,426
11	Arkansas	97,842
12	Kansas	95,298
13	Alabama	93,742
14	Indiana	93,549
15	Mississippi	92,401
16	Idaho	91,077
17	Nevada	90,404
18	Wyoming	89,695
19	Illinois	88,895
20	Montana	87,914
21	Louisiana	86,178
22	South Carolina	86,115
23	California	84,116
24	Georgia	83,377
25	New Mexico	83,012
26	Kentucky	82,113
27	Texas	81,915
28	Minnesota	81,898
29	Missouri	80,397
30	Delaware	80,174
31	Florida	80,146
32	New Jersey	78,452
33	Ohio	76,634
34	Massachusetts	76,028
35	Alaska	73,795
36	New York	73,002
37	North Carolina	72,458
38	Connecticut	70,127
39	Colorado	68,962
40	West Virginia	67,517
41	Pennsylvania	66,258
42	Michigan	60,940
43	Virginia	59,138
44	Maryland	58,661
45	District of Columbia	52,245
46	New Hampshire	48,315
47	Washington	41,467
48	Puerto Rico	39,840
49	Oregon	33,766
50	Maine	29,254
51	Vermont	19,175
52	Hawaii	18,212

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	South Carolina	790
2	Oklahoma	677
3	New York	634
4	New Jersey	583
5	North Carolina	583
6	Arizona	577
7	Delaware	539
8	Florida	524
9	Texas	518
10	Kentucky	513
11	Mississippi	506
12	Georgia	504
13	Arkansas	487
14	Massachusetts	478
15	Alabama	469
16	Pennsylvania	467
17	West Virginia	453
18	Utah	434
19	Virginia	433
20	California	432
21	Tennessee	430
22	Louisiana	409
23	New Mexico	372
24	Nevada	359
25	New Hampshire	352
26	District of Columbia	349
27	Ohio	344
28	Indiana	331
29	Maryland	315
30	Montana	294
31	Wyoming	277
32	Wisconsin	261
33	Iowa	241
34	Vermont	236
35	Kansas	234
36	Missouri	216
37	Maine	215
38	Rhode Island	215
39	Alaska	210
40	Nebraska	199
41	Illinois	196
42	Minnesota	187
43	South Dakota	171
44	Idaho	167
45	Washington	163
46	Colorado	142
47	North Dakota	142
48	Oregon	129
49	Connecticut	117
50	Michigan	115
51	Puerto Rico	113
52	Hawaii	76

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,418
2	New York	2,219
3	Massachusetts	2,114
4	Rhode Island	2,033
5	Mississippi	2,031
6	South Dakota	2,009
7	Connecticut	1,976
8	Louisiana	1,905
9	North Dakota	1,898
10	Arizona	1,802
11	Pennsylvania	1,696
12	Illinois	1,677
13	Arkansas	1,613
14	Alabama	1,567
15	New Mexico	1,566
16	Iowa	1,553
17	Michigan	1,553
18	Indiana	1,481
19	Tennessee	1,399
20	Nevada	1,386
21	South Carolina	1,367
22	Georgia	1,297
23	Kansas	1,297
24	District of Columbia	1,293
25	Texas	1,284
26	Florida	1,232
27	Maryland	1,178
28	Missouri	1,169
29	Montana	1,154
30	West Virginia	1,129
31	Delaware	1,119
32	Minnesota	1,111
33	Wisconsin	1,105
34	Nebraska	1,039
35	California	1,035
36	Wyoming	1,029
37	Colorado	991
38	Idaho	966
39	Ohio	956
40	Oklahoma	896
41	North Carolina	894
42	Kentucky	890
43	New Hampshire	777
44	Virginia	757
45	Washington	573
46	Puerto Rico	572
47	Utah	519
48	Oregon	467
49	Maine	438
50	Alaska	345
51	Hawaii	287
52	Vermont	278

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Iowa	38
2	Alabama	23
3	Arizona	11
4	California	11
5	Georgia	11
6	Massachusetts	11
7	Mississippi	11
8	Oklahoma	10
9	Tennessee	10
10	Arkansas	9
11	New Mexico	9
12	New York	9
13	Pennsylvania	9
14	Texas	9
15	Kentucky	8
16	Louisiana	8
17	Nevada	8
18	New Hampshire	8
19	North Carolina	8
20	South Carolina	8
21	West Virginia	7
22	Florida	6
23	Kansas	6
24	Montana	6
25	New Jersey	6
26	Virginia	6
27	Delaware	5
28	District of Columbia	5
29	Maine	5
30	South Dakota	5
31	Wisconsin	5
32	Illinois	4
33	Indiana	4
34	Maryland	4
35	Michigan	4
36	Ohio	4
37	Utah	4
38	Minnesota	3
39	Rhode Island	3
40	Colorado	2
41	Connecticut	2
42	Nebraska	2
43	North Dakota	2
44	Oregon	2
45	Puerto Rico	2
46	Missouri	1
47	Vermont	1
48	Washington	1
49	Alaska	0
50	Hawaii	0
51	Idaho	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	297,476	1	99
Chattahoochee	Georgia	245,439	2	99
Dewey	South Dakota	235,743	3	99
Lake	Tennessee	235,462	4	99
Lincoln	Arkansas	234,260	5	99
Davidson	Tennessee	120,930	251	92
Richland	South Carolina	86,218	1349	57
York	South Carolina	78,974	1718	45
Orange	California	77,456	1783	43
Pierce	Washington	38,898	2894	7

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	8,346	1	99
Jerauld	South Dakota	7,948	2	99
Buffalo	South Dakota	6,626	3	99
Dickey	North Dakota	6,568	4	99
Gregory	South Dakota	6,452	5	99
Davidson	Tennessee	1,053	1917	38
Richland	South Carolina	1,020	1971	37
Orange	California	964	2043	34
York	South Carolina	904	2119	32
Pierce	Washington	516	2650	15

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.